Demand continues to expand despite the shrinking Brazilian labor market. "Crisis? What a crisis” will likely say a qualified data scientist.
Rare because it is not that easy to find professionals who meet the triad required by the profession: knowledge of programming, statistics/mathematics and business acumen.
“It is a new career in Brazil, and we are seeing an exponential increase in the demand for professionals. On the other hand, we still don't have many people being trained in this area”, says Henrique Gamba, general director of Yoctoo, recruitment specialized in IT.
According to him, the crisis did not bring down salaries for these professionals: from 9,000 reais for those with between 3 and 4 years of experience and up to 22,000 for a specialist in the area. “The generation, capture and storage of data, in increasing volume (big data), is the key to the direction and strategy of any business”, says Gamba.
The companies that hire the most data scientists are technology solution providers (big bat) and those that work intensively with data such as financial institutions, research institutes, internet, e-commerce, credit bureaus, etc.
The different phases of a data scientist's work
“The heart of a data scientist's activity is to analyze a mass of information to make inferences”, says Lucas de Paula of Neoway, a company that offers business intelligence solutions.
The data is the scientist's raw material. But your first challenge is to think of the right question to ask to get the answer the business needs to answer.
It is from this question that the work of capturing and preparing the data begins. With the ground ready, it's time to apply mathematical formulas. For this, statistical models are elaborated and hypotheses created and tested.
“He uses the raw material (data) to extract insights, generate these hypotheses that will be put to the test with statistical and mathematical work”, says Lucas. The scientific validation of these inferences is what will actually bring the business value response. The next step is to present the findings to external and internal customers. “Knowing how to communicate is equally important. It is often necessary to convince the customer that the model is accurate,” he says.
Analyzing the data and its conclusions is like combining notes by playing the piano and producing a song, compares Lucas. But to play this piano, you have to first load it and make it ready for use, which, in the language of data science, means preparing the collected information.
“The difference between a statistician and a data scientist is that the first needs the data ready to work and the second has the versatility to do this preparation, there are so-called hacking skills (hacking skills), which the statistician does not have.
Combination of Skills and Teamwork
Precisely because it is extremely difficult to combine all these skills, the most common is to form teams with professionals who complement each other.
“We mix and build teams. A computer professional is in charge of the preparation, works together with those who do mathematical analysis and we also build in business knowledge”, says Monica Tyszler, director of solutions and services at SAS Latin America.
Lack of any of the skills compromises all success on the job. “If there is expertise in computing and mathematics, but none in business, it will not be possible to find out what problem the company needs to solve,” says De Paula.
More expertise in computing and business and none in math and statistics will result in detrimental accuracy of the analysis. And finally, the lack of computing expertise prevents the extraction of a significant amount of data that is ultimately what has the ability to bring business value.
At Neoway, De Paula forms work pairs to ensure all skills are on the table. “For example, we have here a physicist and a specialist in econometrics who work together”, he says.
The most frequent formations of data scientists in action today
In the United States, some universities already have reputable training programs. But because it is a new activity, most professionals who work in the area today came from other academic backgrounds.
Statistics, engineering, mathematics, physics are quite common in professionals' curricula, according to Yoctoo's general director. “It is important to note that courses such as master's and doctorate are almost always mandatory”, says Gamba. According to him, careers that facilitate migration to the area are business intelligence, statistics or technology, in general.
In the opinion of the director of SAS, training in production engineering linked to computer engineering provides a good basis for anyone looking to pursue a career in the field of data science. “There are classes in programming, statistical mathematics, research and there is business knowledge applied to production engineering”, he explains.
Knowing how to program and having knowledge of database management systems is essential. Most of the languages used in this branch are open source, that is, open source. At Neoway, teams work with the languages Python, R, Spark, Scala, Go and the database managers MongoDB, SQL, Elasticsearch, Neo4j, Cassandra and the Apache Kafka messaging system.
SAS is one of the companies that invest in professional qualification. “We started with a data scientist training course in the United States and the idea was to bring it to Brazil”, says Mônica. Big data management techniques, advanced analytics, data visualization, machine learning and communication techniques that are also essential for data scientists, the SAS Academy for Data Science is an option for anyone seeking certification in this area.
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